The Internet-of-Things (IoT) is a network of physical objects, such as devices and sensors, that enables the physical objects to collect and exchange data. The Internet-of-Things has contributed to the growth of the number of sensors and the corresponding amount of data generated by the sensors in residential and industrial environments. By collecting data generated from the sensors, a large number of time series data tends to be available for processing.
An objective of a time series analysis is to explain the behavior of a series using past values of the time series. Many common behavior models are univariate, that is, a series is modelled using only its own previous values as explanatory variables. In a multivariate setting, a time series is explained by its own past values, as well as the past and present values of other series of interest. For example, industrial plants often have thousands of sensors designed to monitor the status of a given plant and feed automated control systems. It is important to estimate the relationship between a given performance indicator and a set of variables (sensor streams). This may reveal the underlying structure of the physical model and allows the current and optimal operational conditions of the plant to be assessed. When the outcome of an industrial process is influenced by many different inputs, predictive models can help to identify the best actions to optimize the outcome in different situations. In addition, models to explain a target time series in specific situations can be used to monitor an industrial process so that detachment between predicted and observed values indicate potential problems to be solved.
In order to derive intelligence from the collected data, a need exists for the ability to process a large number of large-scale time series and compute reasonable multivariate models in real-time.